I stumbled onto this article from Carol Ozemhoya and wanted to share it with you all as it’s very telling of the Ivanka Trump brand and its decline of popularity. Below is her full article and also the Press Release for Vector.
When someone is in the news as much as President Donald J. Trump, you’d think his daughter’s clothing line would gain popularity. But instead, it miserably failed, much, it seems, to the surprise of just about everyone, including its stockholders. The line, which was dropped by high-end retailer Nordstrom, followed by a reduction in promotions by TJ Maxx and Marshalls, is listed on the stock market as JWN.While the move to drop the line seems to coincide with a social media campaign that encouraged consumers to boycott the brand – called #Grab Your Wallet – Nordstrom immediately claimed the move came because of poor sales, adding that it drops about 10 percent of brand name products a year.
The President didn’t just sit by, going on social media himself and criticizing Nordstrom for treating his daughter “unfairly.” The stock reacted and the stock fell dramatically but recovered by the end of the day after the New York Times reported that retailers were told to move the products to be less conspicuous.
All this panic and drama could have been avoided with the use of artificial intelligence and its sentiment subjectivity analysis via Vector. Social media often moves faster than the news, but the thing is there are now tools in place that can use those very postings to predict the comings and goings of businesses, transactions and yes, even the stock market. The Nordstrom stock price reaction to President Trump’s tweet can be largely explained with a simple sentiment subjectivity analysis study using Vector.
Recently developed tools would have utilized sentiment analysis on the phrase “Ivanka Trump” in news articles as well as social media. The indication of negative sentiment would have sent up a red flag and given analysts and other interested parties the chance to prepare for and perhaps even prevent the stock drop.
In addition, these artificial intelligence tools would also have realized a correlation between Trump’s daughter’s product line, its revenue and weakening of the brand. It took almost a whole day for the stock to recover, but the near fiasco could have been prevented in the first place if the company had been using Vector’s sentiment analysis tools.
Using Vector’s tools, you can run sentiment polarity analysis to determine if there are strongly negative feelings about a person, organization, geopolitical entity or brand… in this case, the Ivanka Trump brand.
Here’s another example. It’s entirely possible that the transportation company Lyft picked up on sentiment analysis in a recent boost in popularity.
In this environment, staying neutral over the biggest political controversies, such as the travel ban, can be more dangerous to a company than taking sides; losing a moderate share of the big anti-Trump majorities in these places is a bigger risk than losing a larger share of the small group of his supporters. Uber didn’t immediately grasp this lesson. But Lyft, its smaller rival, did. After the immigration order was executed, the firm quickly announced a $1 million donation to the American Civil Liberties Union, which was fighting the ban. Hordes of progressive Uber users made the switch to Lyft.”
It’s a different world out there from when your dad or grandparents started the company. The Internet has changed the game, and Vector has new plays to enable any business owner the capacity to keep his brand hot to the sentiments of the buying public.
Press Release About Vector
Vector Transforms Internet into Goldmine of Invaluable Quantifiable Data Utilizing real-time quantitative research and advanced algorithms, Vector stands apart from existing analytical platforms by extracting more content, with greater efficiency, than has ever been possible before.
Houston, Texas – Feb. 8, 2017 – Indexer, a Houston-based technology start-up, today announced the public availability of Vector, a groundbreaking analytical platform that transforms the internet into unprecedented tangible, meaningful data. Utilizing real-time quantitative research and advanced algorithms, Vector stands apart from existing analytical platforms by extracting more content, with greater efficiency, than has ever been possible before.
Monitoring half-a-billion news sources simultaneously with Natural Language Processing (NLP), including social media, Vector precisely searches speech patterns and accurately translates languages to dig deep into the core of the internet; resulting in the extraction of all facets of information, including unstructured text and images.
“Vector provides feature-vector transformations of text, which are an integral feedstock that fuels the customized algorithms we design,” explains Vector’s co-founder, Anton Gordon, an award-winning data scientist, machine learning researcher and commodities expert. “The outcome is unparalleled aid to economists, traders, scientists, and executives for predicting market price movements.”
In addition to asset price predictions, Vector identifies the primary organizations, individuals and geopolitical entities that influence asset prices, markets and customers most. This helps investors to decipher which sell-side equity analysts and economists to be influenced by.
“While several quantitative financial tools exist, they are primarily designed to examine historical data to predict future prices. Little progress has been made in the processing of real-time, unstructured qualitative data into a more useful quantitative format until now,” says Gordon. “We recognize that unstructured content holds the key to understanding hidden demand for products and brands,” says Jo Fletcher, Indexer’s head of data visualization.
In addition to data extraction, Fletcher believes that Vector’s subjectivity and polarity analysis could provide the key in filtering non-fact-based news content (commonly referred to as “fake news”). Key Features
- Sentiment analysis to determine if the writer is positive, negative or neutral on a topic;
- Real-time text analysis to receive access to millions of articles per day;
- Summarization of text to obtain a summary of the most important points of the text;
- Collaborative learning to solve problems as a team using saved searches and messaging;
- LDA topic extraction to automatically discover topics contained in millions of documents;
- Faceted news search to explore a collection of information by applying multiple filters;
- API Access for developers and quantitative analysts;
- Feature vectors to use n-dimensional feature vectors to represent a document;
- Real-time updates to receive dynamic analysis and real-time intelligence.
Applications for Vector include: Technology, finance, academia, biostatistics, law, healthcare, entertainment/media.
Vector is available on a monthly or annual subscription basis